Methods and systems for characteristic leveling

ABSTRACT

Characteristic normalization (or leveling) is a process that yields consistent and equitable characteristic definitions across multiple sources of credit information. This leveling ensures that when the same data is present for multiple credit sources, for example two or more credit reporting agencies, it is interpreted in the same manner, acknowledging that differences in the data itself may still be present.

CROSS REFERENCE TO RELATED PATENT APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.11/685,070 filed Mar. 12, 2007, now U.S. Pat. No. 7,801,812, whichclaims priority to U.S. Provisional Application No. 60/781,138 filedMar. 10, 2006, U.S. Provisional Application No. 60/781,052 filed Mar.10, 2006, and U.S. Provisional Application No. 60/781,450 filed Mar. 10,2006, all of which are herein incorporated by reference in theirentireties. Related U.S. Utility application Ser. No. 11/685,066, filedMar. 12, 2007, by Morris, et al., titled “Methods and Systems forMulti-Credit Reporting Agency Data Modeling” and U.S. Utilityapplication Ser. No. 11/685,061, filed Mar. 12, 2007 by Morris, et al.,titled “Methods and Systems for Segmentation Using Multiple DependentVariables” are herein incorporated by reference in their entireties.

BACKGROUND OF THE INVENTION

Credit characteristics are a major foundation of consumer creditdecisioning. Inconsistent or inequitable definitions in characteristicsacross Credit Reporting Agencies (CRAs) results in vastly different riskperspectives when decisioning. In particular, this is true for themajority of consumers whose credit profile is housed by more than oneCRA.

It is a common practice that a CRA independently defines characteristicsthat take advantage of their own credit reporting structure. Ifrequired, the CRA then takes the characteristic definitions and makesthe best attempt to “fit” data from another CRA to those definitions.Since the characteristics are written to maximize use of the informationfrom the CRA's own data structure, it may prove challenging (or evenimpossible) to apply the definitions to another CRA's data. Further,while one CRA may have peripheral knowledge of the others' data, the CRAdoes not have access to all the detailed knowledge that an insider tothe other organization would have. This detailed knowledge includes pastupdates (with timing) to the reporting structure, plans for changes tothe reporting structure, detailed definitions and/or intended uses fordata elements, etc.

It is not uncommon that consumer credit grantors have their market shareof credit profile requests spread across multiple credit reportingagencies. Independent of the agency from which the credit informationoriginated, it is desirable that grantors have the ability to makeuniform credit decisions.

SUMMARY OF THE INVENTION

Characteristic normalization (or leveling) is a process that yieldsconsistent and equitable characteristic definitions across multiplesources of credit data. This leveling ensures that when the same data ispresent for multiple credit sources, for example two or more CRAs, it isinterpreted in the same manner, acknowledging that differences in thedata itself may still be present.

For consumer credit grantors, using normalized characteristics andcharacteristic definitions allows them to have a more consistent“picture” of a consumer's credit payment behavior, regardless of whichCRA's data is being used. Credit grantors can have more confidence thatthey are making a consistent credit decision when applying the samecharacteristics to different sets of data. Normalized characteristicsare also beneficial in helping credit grantors avoid large investmentsto maintain different credit decisioning policies for each CRA. Forexample, if a credit grantor were to retain different credit policiesbased on the CRA supplying the profile, they would need to investresources in programming and maintaining the different versions ofpolicies, as well as training personnel to use the different versions ofpolicies, rather than one set.

For the consumer seeking credit, they can be assured that no matterwhich agency provides their credit profile, they are getting a uniformrepresentation of their credit history. This is very important in a timewhen educated consumers are conscientiously making inquiries to allthree CRAs for copies of their credit profiles and have the ability tocompare the reported information.

With ever-increasing consumer awareness of “credit scoring,” it isimportant that steps be taken to make consistent credit decisions.Provided are methods and systems for characteristic normalization thatproduces equitable and consistent characteristic definitions, whichcreates a more equitable and consistent score and characteristic resultsfor the same consumer.

Additional advantages of the invention will be set forth in part in thedescription which follows or may be learned by practice of theinvention. The advantages of the invention will be realized and attainedby means of the elements and combinations particularly pointed out inthe appended claims. It is to be understood that both the foregoinggeneral description and the following detailed description are exemplaryand explanatory only and are not restrictive of the invention, asclaimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate embodiments of the invention andtogether with the description, serve to explain the principles of theinvention.

FIG. 1 is a flowchart illustrating an exemplary method forcharacteristic leveling;

FIG. 2 is a flowchart illustrating an exemplary method forcharacteristic leveling;

FIG. 3 is a flowchart illustrating an exemplary method for credit dataauditing;

FIG. 4 is an exemplary operating environment.

DETAILED DESCRIPTION OF THE INVENTION

Before the present methods and systems are disclosed and described, itis to be understood that this invention is not limited to specificsynthetic methods, specific components, or to particular compositions,as such may, of course, vary. It is also to be understood that theterminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting.

As used in the specification and the appended claims, the singular forms“a,” “an” and “the” include plural referents unless the context clearlydictates otherwise.

Ranges may be expressed herein as from “about” one particular value,and/or to “about” another particular value. When such a range isexpressed, another embodiment includes—from the one particular valueand/or to the other particular value. Similarly, when values areexpressed as approximations, by use of the antecedent “about,” it willbe understood that the particular value forms another embodiment. Itwill be further understood that the endpoints of each of the ranges aresignificant both in relation to the other endpoint, and independently ofthe other endpoint.

“Optional” or “optionally” means that the subsequently described eventor circumstance may or may not occur, and that the description includesinstances where said event or circumstance occurs and instances where itdoes not.

The present invention may be understood more readily by reference to thefollowing detailed description of preferred embodiments of the inventionand the Examples included therein and to the Figures and their previousand following description.

I. Method

True characteristic normalization is a process that involves acooperative effort by “credit data experts” from each of the involvedCRAs. These experts understand the scoring objectives and underlyingphilosophies for the desired set of characteristics. A set ofcharacteristics and characteristic definitions can be generated thatmost align the definitions for all the CRAs to reflect the objectives. Acharacteristic can be a data element that summarizes a particularcharacteristic of a consumer's credit behavior as derived from a creditreport (i.e. Total number of open and closed bankcard trades). Acharacteristic definition can be a statement that describes the meaningof the characteristic through components (e.g. filters to be used, fieldlength, default conditions) needed to calculate the characteristicvalues on each CRA's credit file. Filters can be comprehensivedescriptions that are utilized in characteristic definitions (i.e.BNKCARD=specific elements that identify Bankcard at each CRA). Filtersclassify data elements from a credit report into specific categoriessuch that they may be used in any combination to create characteristics.

Characteristics can be composed of a set of building blocks (filters)that define basic concepts used in credit decisioning. These conceptsinclude, but are not limited to, industry types (e.g. banking, finance,retail), payment behavior (e.g. 30-day rating, presence of bankruptcy,satisfactory rating), credit inquiries initiated by the consumer, andthe like. Characteristics can be defined using these filters. Forexample, a characteristic “Total Number of 30-Day Ratings on RetailAccounts” would use the retail and 30-day rating filters.

FIG. 1 illustrates an exemplary embodiment of the present invention. Atblock 101, a set of characteristics can be generated. Thecharacteristics can be generated from pre-existing characteristics.These characteristics can also be generated through communicationsbetween credit data experts. The corresponding definition for eachcharacteristic can be retrieved from each CRA to generate multiple setsof characteristic definitions, at least one set per CRA. If a newcharacteristic is generated, a corresponding characteristic definitioncan be generated.

The set of characteristics can be generated to accomplish an objectivefor the characteristic set, such as use with prescreen criteria, accountmanagement criteria, credit risk modeling, and the like. At block 102the characteristic set can be reviewed and modified in light of theobjective and the likelihood of leveling success. Also at block 102, theset of characteristic definitions from each CRA can be reviewed andmodified if necessary based on the data elements available for each CRA.The review can be performed by two or more CRAs working collaboratively.

If not retrieved electronically, the characteristic set and the sets ofcharacteristic definitions are programmed (coded). At block 103, eachCRA can retrieve credit data (credit data profiles) for a selectedsample of consumers (in an anonymous manner), applying thecharacteristic set and that CRA's respective characteristic definitionsto those profiles. The result can be one or more audit statistics foreach credit data profile, cumulative audit statistics for the creditdata profiles from each CRA, overall statistics for all the CRAs, andthe like.

Each CRA can generate summary characteristic and filter statistics (e.g.minimum, maximum, mean, median, and quantile values). The statisticvalues for both individual filters and characteristics for individualconsumer profiles can be compared within a CRA, between the CRAs, andbetween iterations of statistic generation. Since the filters summarizea consumer's credit data at a component level rather than at an overallprofile level additional statistics can be generated to audit at theconsumer level.

Table 1 provides exemplary characteristic statistics. By way of example,four characteristics are provided. For each characteristic, the meanvalue of the credit data is provided from each CRA.

TABLE 1 CRA 1 CRA2 CRA3 CHARACTERISTIC MEAN MEAN MEAN Total number ofauto trades ever derogatory 0.19 0.19 0.19 Total number of open bankcardtrades 2.72 2.75 2.76 reported within 6 months Total number of open andclosed bankcard 4.01 3.99 4.05 trades Total number of auto trades openedwithin 1 0.98 1.11 24 months

Table 2 provides exemplary filter statistics. By way of example, fourfilters are provided. Auto can represent financed automobiles, bankcardcan represent open financial accounts, closed can represent closedfinancial account, and charge off can represent financial accounts thathave been “written off” by a creditor. For each filter, the percentageof consumers from the credit data matching that filter is provided, asis the corresponding number of consumers that make up that percentage,for each CRA.

TABLE 2 CRA 1 CRA 1 CRA 2 CRA 2 CRA 3 CRA 3 FILTER % # % # % # Auto64.29 101112 63.25 103332 66.19 120001 Bankcard 72.22 152010 71.01151669 72.29 150216 Closed 51.73 99437 51.79 99997 51.21 98661 ChargeOff 7.25 12107 6.99 11999 8.21 13567

At block 104, the audit statistics can be audited. The audit process isdescribed in more detail below in FIG. 3. At block 105, a check can beperformed to determine if the audit was successful. If the result of theaudit was that the audit statistics returned from the CRAs based on thecharacteristic set and the sets of characteristic definitions providessubstantially consistent results, then the audit was successful. If, atblock 105, an audit has been previously performed, the statisticsgenerated from the previous audit can be compared to the statisticsgenerated during the current audit. Statistics can be compared betweenCRAs and within CRAs. A successful audit is indicated when statisticsare substantially consistent. For example, values generated for astatistic that represents total balances on open revolving traits can bedeemed to be substantially consistent when the difference in values isfrom about 0% to about 5%. Similarly, values generated for a statisticthat represents presence of bankruptcy public record can be deemed to besubstantially consistent when the difference in values is from about 0to about 0.1. Another example includes values generated for a statisticthat represents total number of open trades can be deemed to besubstantially consistent when the difference in values is from about 0to about 100,000. One skilled in the art will recognize that there arevarious statistics that can be generated and as such, what definessubstantially consistent values can vary depending on the statisticgenerated.

Then, at block 106, a normalized set of characteristic definitionscorresponding to the set of characteristics can be generated. Thenormalized set of characteristic definitions can be translated to applyto each CRA's unique data management system for use in a multi-CRAscoring model.

If, however, at block 105, the audit was not successful, then the auditstatistics based on the characteristic set and the sets ofcharacteristic definitions did not provide substantially consistentresults. Block 102 can be returned to and the process repeated until theaudit is successful.

Based on the results of the filter and characteristic comparison betweenCRAs, the characteristic set and sets of characteristic definitions maybe modified to accommodate scenarios not addressed by the originalcharacteristic set and sets of characteristic definitions but that arepresent in the data. More importantly, the characteristic set and setsof characteristic definitions may be modified to further minimizedifferences between the audit statistics provided by the CRAs, whilemaintaining the agreement in credit perspectives. Summary filter andcharacteristic statistics can be compared to previously generatedsummary filter and characteristic statistics.

The auditing may require several iterations before a final set ofcharacteristics and a final set of normalized characteristic definitionsare generated.

As an example of this process, it can be determined that a filter for“trade” was needed since many of the characteristics calculate values onthe trade set of the credit profile. Since there are differences betweenCRAs in their display of “trade,” normalization must occur. By way ofexample, the difference can be that one CRA displays their externalcollections as trades while the other two CRAs display them in a segmentseparate from trade. In order to normalize the trade definition, it canbe determined that external collections should be excluded in the tradedefinition for all CRAs.

Furthermore, it can be determined that factoring company data should beincluded as external collections and thus excluded from the trade filterdefinition. The result of this normalization of the trade filtertranslates into CRA specific characteristic definitions for the tradefilter which contain codes unique to each CRA, but provide a consistentend result across them. Exemplary codes are described in Table 3.

TABLE 3 DEFINITION DEFINITION DEFINITION FOR FOR FOR FILTER CRA 1 CRA 2CRA 3 TRADE TR-PT and Id = 07 and TR-TR and Excludes external (ExcludeIND- Status >01 and (Exclude IND- collection trades TYPE = FY, (ExcludeKOB = CODE = Y and factoring YA, YC) YA, YC, YL, or Loan company YZ, ZYType = FC) trades Or Enhanced Type = 48, 0C)

By way of example, in Table 4 the characteristic “Age, in months, ofoldest trade” uses the trade filter that has been normalized. All otheraspects of the characteristic definition, the logic used, the length ofthe field, the default logic handling, etc., are consistent between theCRAs so that the characteristics yield equitable results whenimplemented, baring differences in the core data on the credit profileat each CRA.

TABLE 4 Age, in months, of oldest trade SAS Label: AGE OF OLDEST TRADELogic: TRADE and (OPEN or CLOSED or STATIC) and MONTHS- OPEN <= 9998Computation: MAX Operand1: MONTHS-OPEN Length: 4 Operand2: Default1:9999 Default1 Condition: TR6001 = Default2: 0 and TR0102 = 0 Default3:Default2 Condition: Default3 Condition:

FIG. 2 illustrates another exemplary method for leveling a set of creditdata characteristics. At block 201, a credit data profile can beretrieved for at least one consumer, wherein retrieving a credit dataprofile is performed by at least two of a plurality of credit reportingagencies resulting in at least two credit data profiles for the at leastone consumer. Then at block 202, a set of characteristics can begenerated. At block 203, a respective characteristic definition can beretrieved from each of the at least two of the plurality of creditreporting agencies for each credit characteristic in the set of creditcharacteristics. At block 204, the set of credit characteristics andrespective characteristic definitions can be reviewed, wherein reviewingis performed by the at least two of the plurality of credit reportingagencies. Reviewing the set of credit characteristics and respectivecharacteristic definitions can comprise modifying one or more of therespective characteristic definitions.

At block 205, the respective characteristic definitions can be appliedto each of the retrieved credit data profiles, generating auditstatistics associated with each credit data profile. Generating auditstatistics can comprise generating a first set of audit statistics basedon a first application of the respective characteristic definitions toeach of the retrieved credit data profiles. Generating audit statisticscan also comprise generating a second set of audit statistics based on asecond application of the respective characteristic definitions(original or modified) to each of the retrieved credit data profiles.

At block 206, the audit statistics can be audited. Auditing the auditstatistics can comprise receiving first audit statistics and comparingthe first audit statistics between different credit reporting agencies.Comparing the first audit statistics can comprise modifying at least oneof the respective characteristic definitions. The method can furthercomprise receiving second audit statistics based on the at least onemodified characteristic definition and comparing the first auditstatistics with the second audit statistics. The method can furthercomprise comparing the second audit statistics between different creditreporting agencies. Auditing the audit statistics can further comprisesretrieving and examining one or more credit profiles from the creditdata. The examination can be used to determine the cause of similaritiesor differences between the audit statistics.

Then at block 207, it can be determined whether the audit wassuccessful. Auditing the audit statistics can be deemed successful whenthe first audit statistics are substantially consistent with the secondaudit statistics. Auditing the audit statistics can also be deemedsuccessful when the first audit statistics are substantially consistentbetween different credit reporting agencies. Substantially consistentcan be determined based on the statistic generated. If the audit wassuccessful, a normalized set of credit characteristics andcharacteristic definitions can be generated from the set of creditcharacteristics and respective characteristic definitions at block 208.The normalized set of characteristics and corresponding normalized setof characteristic definitions can be used in a multi-CRA scoring model.If the audit was not successful, the method can return to block 204.

FIG. 3 illustrates steps in an exemplary audit process. At block 301,the audit statistics are compared, and similarities and differencesnoted. At block 302, one or more credit profiles can be retrieved fromthe credit data and examined. The examination can be used to determinethe cause of similarities or differences between the audit statistics.Then, at block 303, the similarities and differences can be used tomodify the respective characteristic definitions so as to provideconsistent data retrieval results across multiple CRAs. The modificationcan be made and approved by at least two CRAs working collaboratively.

II. System

FIG. 4 is a block diagram illustrating an exemplary operatingenvironment for performing the disclosed method. This exemplaryoperating environment is only an example of an operating environment andis not intended to suggest any limitation as to the scope of use orfunctionality of operating environment architecture. Neither should theoperating environment be interpreted as having any dependency orrequirement relating to any one or combination of components illustratedin the exemplary operating environment.

The method can be operational with numerous other general purpose orspecial purpose computing system environments or configurations.Examples of well known computing systems, environments, and/orconfigurations that may be suitable for use with the system and methodinclude, but are not limited to, personal computers, server computers,laptop devices, and multiprocessor systems. Additional examples includeset top boxes, programmable consumer electronics, network PCs,minicomputers, mainframe computers, distributed computing environmentsthat include any of the above systems or devices, and the like.

The processing of the disclosed method can be performed by softwarecomponents. The disclosed method may be described in the general contextof computer-executable instructions, such as program modules, beingexecuted by one or more computers or other devices. Generally, programmodules include computer code, routines, programs, objects, components,data structures, etc. that perform particular tasks or implementparticular abstract data types. The disclosed method may also bepracticed in grid-based and distributed computing environments wheretasks are performed by remote processing devices that are linked througha communications network. In a distributed computing environment,program modules may be located in both local and remote computer storagemedia including memory storage devices. The method may be practicedutilizing firmware configured to perform the methods disclosed herein inconjunction with system hardware.

The methods and systems of the present invention can employ ArtificialIntelligence techniques such as machine learning and iterative learning.Examples of such techniques include, but are not limited to, expertsystems, case based reasoning, Bayesian networks, behavior based AI,neural networks, fuzzy systems, evolutionary computation (e.g. geneticalgorithms), swarm intelligence (e.g. ant algorithms), and hybridintelligent systems (e.g. Expert inference rules generated through aneural network or production rules from statistical learning).

The method disclosed herein can be implemented via a general-purposecomputing device in the form of a computer 401. The components of thecomputer 401 can include, but are not limited to, one or more processorsor processing units 403, a system memory 412, and a system bus 413 thatcouples various system components including the processor 403 to thesystem memory 412.

The system bus 413 represents one or more of several possible types ofbus structures, including a memory bus or memory controller, aperipheral bus, an accelerated graphics port, and a processor or localbus using any of a variety of bus architectures. By way of example, sucharchitectures can include an Industry Standard Architecture (ISA) bus, aMicro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, aVideo Electronics Standards Association (VESA) local bus, and aPeripheral Component Interconnects (PCI) bus also known as a Mezzaninebus. This bus, and all buses specified in this description can also beimplemented over a wired or wireless network connection. The bus 413,and all buses specified in this description can also be implemented overa wired or wireless network connection and each of the subsystems,including the processor 403, a mass storage device 404, an operatingsystem 405, characteristic leveling software 406, credit data 407, anetwork adapter 408, system memory 412, an Input/Output Interface 410, adisplay adapter 409, a display device 411, and a human machine interface402, can be contained within one or more remote computing devices 414a,b,c at physically separate locations, connected through buses of thisform, in effect implementing a fully distributed system.

The computer 401 typically includes a variety of computer readablemedia. Such media can be any available media that is accessible by thecomputer 401 and includes both volatile and non-volatile media,removable and non-removable media. The system memory 412 includescomputer readable media in the form of volatile memory, such as randomaccess memory (RAM), and/or non-volatile memory, such as read onlymemory (ROM). The system memory 412 typically contains data such ascredit data 407 and/or program modules such as operating system 405 andcharacteristic leveling software 406 that are immediately accessible toand/or are presently operated on by the processing unit 403.

The computer 401 may also include other removable/non-removable,volatile/non-volatile computer storage media. By way of example, FIG. 4illustrates a mass storage device 404 which can provide non-volatilestorage of computer code, computer readable instructions, datastructures, program modules, and other data for the computer 401. Forexample, a mass storage device 404 can be a hard disk, a removablemagnetic disk, a removable optical disk, magnetic cassettes or othermagnetic storage devices, flash memory cards, CD-ROM, digital versatiledisks (DVD) or other optical storage, random access memories (RAM), readonly memories (ROM), electrically erasable programmable read-only memory(EEPROM), and the like.

Any number of program modules can be stored on the mass storage device404, including by way of example, an operating system 405 andcharacteristic leveling software 406. Each of the operating system 405and characteristic leveling software 406 (or some combination thereof)may include elements of the programming and the characteristic levelingsoftware 406. Credit data 407 can also be stored on the mass storagedevice 404. Credit data 407 can be stored in any of one or moredatabases known in the art. Examples of such databases include, DB2®,Microsoft® Access, Microsoft® SQL Server, Oracle®, mySQL, PostgreSQL,and the like. The databases can be centralized or distributed acrossmultiple systems, such as across multiple CRAs.

A user can enter commands and information into the computer 401 via aninput device (not shown). Examples of such input devices include, butare not limited to, a keyboard, pointing device (e.g., a “mouse”), amicrophone, a joystick, a serial port, a scanner, and the like. Theseand other input devices can be connected to the processing unit 403 viaa human machine interface 402 that is coupled to the system bus 413, butmay be connected by other interface and bus structures, such as aparallel port, game port, or a universal serial bus (USB).

A display device 411 can also be connected to the system bus 413 via aninterface, such as a display adapter 409. A computer 401 can have morethan one display adapter 409 and a computer 401 can have more than onedisplay device 411. For example, a display device can be a monitor, anLCD (Liquid Crystal Display), or a projector. In addition to the displaydevice 411, other output peripheral devices can include components suchas speakers (not shown) and a printer (not shown) which can be connectedto the computer 401 via Input/Output Interface 410.

The computer 401 can operate in a networked environment using logicalconnections to one or more remote computing devices 414 a,b,c. By way ofexample, a remote computing device can be a personal computer, portablecomputer, a server, a router, a network computer, a peer device or othercommon network node, and so on. Logical connections between the computer401 and a remote computing device 414 a,b,c can be made via a local areanetwork (LAN) and a general wide area network (WAN). Such networkconnections can be through a network adapter 408. A network adapter 408can be implemented in both wired and wireless environments. Suchnetworking environments are commonplace in offices, enterprise-widecomputer networks, intranets, and the Internet 415.

For purposes of illustration, application programs and other executableprogram components such as the operating system 405 are illustratedherein as discrete blocks, although it is recognized that such programsand components reside at various times in different storage componentsof the computing device 401, and are executed by the data processor(s)of the computer. An implementation of application software,characteristic leveling software 406, may be stored on or transmittedacross some form of computer readable media. Computer readable media canbe any available media that can be accessed by a computer. By way ofexample, and not limitation, computer readable media may comprise“computer storage media” and “communications media.” “Computer storagemedia” include volatile and non-volatile, removable and non-removablemedia implemented in any method or technology for storage of informationsuch as computer readable instructions, data structures, programmodules, or other data. Computer storage media includes, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store thedesired information and which can be accessed by a computer.

While this invention has been described in connection with preferredembodiments and specific examples, it is not intended that the scope ofthe invention be limited to the particular embodiments set forth, as theembodiments herein are intended in all respects to be illustrativerather than restrictive.

Unless otherwise expressly stated, it is in no way intended that anymethod set forth herein be construed as requiring that its steps beperformed in a specific order. Accordingly, where a method claim doesnot actually recite an order to be followed by its steps or it is nototherwise specifically stated in the claims or descriptions that thesteps are to be limited to a specific order, it is no way intended thatan order be inferred, in any respect. This holds for any possiblenon-express basis for interpretation, including: matters of logic withrespect to arrangement of steps or operational flow; plain meaningderived from grammatical organization or punctuation; the number or typeof embodiments described in the specification.

It will be apparent to those skilled in the art that variousmodifications and variations can be made in the present inventionwithout departing from the scope or spirit of the invention. Otherembodiments of the invention will be apparent to those skilled in theart from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

1. A computer-implemented method for leveling credit characteristics foruse in a multi-credit reporting agency scoring model comprising: (a)retrieving by a computer a credit data profile for at least oneconsumer; (b) generating by the computer a set of creditcharacteristics; (c) retrieving by the computer a respectivecharacteristic definition for each credit characteristic in the set ofcredit characteristics; (d) reviewing by the computer the set of creditcharacteristics and respective characteristic definitions; (e) applyingby the computer the respective characteristic definitions to each of theretrieved credit data profiles, generating audit statistics; (f)auditing by the computer the audit statistics; wherein one or more ofsteps (a)-(d) are performed by at least two of a plurality of creditreport agencies; (g) generating by the computer a normalized set ofcredit characteristics and characteristic definitions from the set ofcredit characteristics and respective characteristic definitions; and(h) applying by the computer the normalized set of creditcharacteristics and characteristic definitions to a multi-creditreporting agency scoring model, the multi-credit reporting agencyscoring model comprising: retrieving a source list from each of aplurality of credit reporting agencies, each said source list comprisinga list of selected consumers; merging each of the source lists into amerged source list; retrieving credit data from each of the plurality ofcredit reporting agencies for each consumer in the merged source list;adding the retrieved credit data to the merged source list; andgenerating a modeling sample based on the normalized credit data.
 2. Themethod of claim 1, wherein retrieving credit data from each of theplurality of credit reporting agencies for each consumer in the mergedsource list comprises: retrieving by the computer observation creditdata from each of the plurality of credit reporting agencies for eachconsumer in the merged source list, wherein the observation credit datacorresponds to a first date; and retrieving by the computer performancecredit data from each of the plurality of credit reporting agencies foreach consumer in the merged source list, wherein the performance creditdata corresponds to a second date.
 3. The method of claim 1, furthercomprising optionally determining whether the auditing step wassuccessful; and if the step of auditing was not successful, modifying bythe computer one or more of the respective characteristic definitionsand repeating by the computer steps (d), (e), and (f) until the step ofauditing the audit statistics is successful.
 4. The method of claim 1,wherein generating the audit statistics further comprises: generatingfirst audit statistics; and comparing the first audit statistics betweendifferent credit reporting agencies.
 5. The method of claim 4, whereincomparing the first audit statistics comprises modifying at least one ofthe respective characteristic definitions.
 6. The method of claim 5,further comprising: generating second audit statistics based on the atleast one modified characteristic definition; and comparing the firstaudit statistics with the second audit statistics.
 7. The method ofclaim 6, further comprising: comparing the second audit statisticsbetween different credit reporting agencies.
 8. The method of claim 6,wherein auditing the audit statistics is successful when the first auditstatistics are substantially consistent with the second auditstatistics.
 9. The method of claim 6, wherein auditing the auditstatistics is successful when the first audit statistics aresubstantially consistent between different credit reporting agencies.10. A system for leveling credit characteristics for use in amulti-credit reporting agency scoring model comprising: a memoryconfigured for storing credit data profiles and credit reports; aprocessor, coupled to the memory, wherein the processor is configured toperform the steps of: (a) retrieving by a computer a credit data profilefor at least one consumer; (b) generating by the computer a set ofcredit characteristics; (c) retrieving by the computer a respectivecharacteristic definition for each credit characteristic in the set ofcredit characteristics; (d) reviewing by the computer the set of creditcharacteristics and respective characteristic definitions; (e) applyingby the computer the respective characteristic definitions to each of theretrieved credit data profiles, generating audit statistics; (f)auditing by the computer the audit statistics; wherein one or more ofsteps (a)-(d) are performed by at least two of a plurality of creditreport agencies; (g) generating by the computer a normalized set ofcredit characteristics and characteristic definitions from the set ofcredit characteristics and respective characteristic definitions; and(h) applying by the computer the normalized set of creditcharacteristics and characteristic definitions to a multi-creditreporting agency scoring model, the multi-credit reporting agencyscoring model comprising: retrieving a source list from each of aplurality of credit reporting agencies, each said source list comprisinga list of selected consumers; merging each of the source lists into amerged source list; retrieving credit data from each of the plurality ofcredit reporting agencies for each consumer in the merged source list;adding the retrieved credit data to the merged source list; andgenerating a modeling sample based on the normalized credit data. 11.The system of claim 10, wherein retrieving credit data from each of theplurality of credit reporting agencies for each consumer in the mergedsource list comprises: retrieving by the computer observation creditdata from each of the plurality of credit reporting agencies for eachconsumer in the merged source list, wherein the observation credit datacorresponds to a first date; and retrieving by the computer performancecredit data from each of the plurality of credit reporting agencies foreach consumer in the merged source list, wherein the performance creditdata corresponds to a second date.
 12. The system of claim 10, furthercomprising optionally determining whether the auditing step wassuccessful; and if the step of auditing was not successful, modifying bythe computer one or more of the respective characteristic definitionsand repeating by the computer steps (d), (e), and (f) until the step ofauditing the audit statistics is successful.
 13. The system of claim 10,wherein generating the audit statistics further comprises: generatingfirst audit statistics; and comparing the first audit statistics betweendifferent credit reporting agencies.
 14. The system of claim 13, whereincomparing the first audit statistics comprises modifying at least one ofthe respective characteristic definitions.
 15. The system of claim 14,further comprising: generating second audit statistics based on the atleast one modified characteristic definition; and comparing the firstaudit statistics with the second audit statistics.
 16. The system ofclaim 15, further comprising: comparing the second audit statisticsbetween different credit reporting agencies.
 17. The system of claim 15,wherein auditing the audit statistics is successful when the first auditstatistics are substantially consistent with the second auditstatistics.
 18. The system of claim 15, wherein auditing the auditstatistics is successful when the first audit statistics aresubstantially consistent between different credit reporting agencies.19. A non-transitory computer-readable storage medium withcomputer-executable instructions embodied thereon for leveling creditcharacteristics for use in a multi-credit reporting agency scoring modelcomprising: (a) retrieving a credit data profile for at least oneconsumer; (b) generating a set of credit characteristics; (c) retrievinga respective characteristic definition for each credit characteristic inthe set of credit characteristics; (d) reviewing the set of creditcharacteristics and respective characteristic definitions; (e) applyingthe respective characteristic definitions to each of the retrievedcredit data profiles, generating audit statistics; (f) auditing theaudit statistics; wherein one or more of steps (a)-(d) are performed byat least two of a plurality of credit report agencies; (g) generating anormalized set of credit characteristics and characteristic definitionsfrom the set of credit characteristics and respective characteristicdefinitions; and (h) applying the normalized set of creditcharacteristics and characteristic definitions to a multi-creditreporting agency scoring model, the multi-credit reporting agencyscoring model comprising: retrieving a source list from each of aplurality of credit reporting agencies, each said source list comprisinga list of selected consumers; merging each of the source lists into amerged source list; retrieving credit data from each of the plurality ofcredit reporting agencies for each consumer in the merged source list;adding the retrieved credit data to the merged source list; andgenerating a modeling sample based on the normalized credit data.